1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
27/7/2025 Electricidad 52000 Andrés NA
27/7/2025 Comida 59147 Tami Supermercado
29/7/2025 Comida 10000 Andrés complemento comida
29/7/2025 Electrodomésticos/mantención casa 68000 Andrés NA
30/7/2025 Comida 24140 Tami Supermercado
31/7/2025 Gas 19100 Andrés NA
3/8/2025 Comida 86089 Tami Supermercado
10/8/2025 Comida 108649 Tami Supermercado
12/8/2025 Enceres 13490 Tami Confort
16/8/2025 VTR 22000 Andrés NA
17/8/2025 Comida 72586 Tami Supermercado
20/8/2025 Electricidad 65242 Andrés 49393- 42306 -47872= 1521
25/8/2025 Comida 35500 Andrés ida al super
25/8/2025 Comida 35000 Andrés piwen
27/8/2025 Comida 37314 Tami Supermercado
30/8/2025 Comida 67203 Tami Supermercado
31/8/2025 Diosi 28000 Andrés american litter
1/9/2025 Comida 7800 Andrés piwen
7/9/2025 Comida 93538 Tami Supermercado
10/9/2025 Diosi 49990 Andrés braloy 7.5 kg
13/9/2025 Comida 60499 Tami Supermercado
23/9/2025 Comida 76691 Tami Supermercado
28/9/2025 Comida 87200 Tami Supermercado
30/9/2025 Gas 82000 Andrés 79500+propina
24/9/2025 Electrodomésticos/mantención casa 40000 Andrés mantención calefont 50 mil
23/9/2025 Gas 80000 Andrés gas 79 mil + propina
30/9/2025 VTR 22000 Andrés NA
4/10/2025 Comida 67440 Tami Supermercado
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  theme_custom() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))

tsData_gastos = decompose(tsData_gastos)

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7  # Weekly data
num_periods <- length(tsData_gastos$x)  # Total number of periods in your time series

# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)

# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
  day = dates,
  Actual = as.numeric(tsData_gastos$x),
  Seasonal = as.numeric(tsData_gastos$seasonal),
  Trend = as.numeric(tsData_gastos$trend),
  Random = as.numeric(tsData_gastos$random)
)

tsData_gastos_long <- tsData_gastos_df %>%
  pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"), 
               names_to = "Component", values_to = "Value")

# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
  facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
  theme(strip.text = element_text(size = 12))

#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 1.0429e+09   2    5.4216 0.0046 ** 
## lag_depvar    2.6496e+11   1 2754.8426 <2e-16 ***
## Residuals     8.2907e+10 862                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff       lwr      upr     p adj
## 1-0  7228.838 -1728.424 16186.10 0.1407873
## 2-0 31456.791 23404.047 39509.53 0.0000000
## 2-1 24227.953 19575.584 28880.32 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
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## 29   28706.00             0   28640.00
## 30   28331.57             0   28706.00
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## 708  98395.14             2   95424.29
## 709 115594.71             2   98395.14
## 710 114267.57             2  115594.71
## 711  88353.29             2  114267.57
## 712  88750.86             2   88353.29
## 713  78835.71             2   88750.86
## 714  75519.14             2   78835.71
## 715  73202.86             2   75519.14
## 716  53433.29             2   73202.86
## 717  48165.71             2   53433.29
## 718  52163.14             2   48165.71
## 719  49306.86             2   52163.14
## 720  36846.86             2   49306.86
## 721  43220.57             2   36846.86
## 722  38952.29             2   43220.57
## 723  41522.29             2   38952.29
## 724  39090.00             2   41522.29
## 725  28452.57             2   39090.00
## 726  32975.00             2   28452.57
## 727  33690.71             2   32975.00
## 728  26405.29             2   33690.71
## 729  47087.43             2   26405.29
## 730  49660.29             2   47087.43
## 731  47409.71             2   49660.29
## 732  53881.71             2   47409.71
## 733  45189.57             2   53881.71
## 734  45503.86             2   45189.57
## 735  54640.14             2   45503.86
## 736  39131.29             2   54640.14
## 737  35024.14             2   39131.29
## 738  44755.43             2   35024.14
## 739  41063.29             2   44755.43
## 740  42783.29             2   41063.29
## 741  45952.57             2   42783.29
## 742  44937.43             2   45952.57
## 743  40838.43             2   44937.43
## 744  48838.43             2   40838.43
## 745  43139.14             2   48838.43
## 746  67134.29             2   43139.14
## 747  73224.29             2   67134.29
## 748  68770.71             2   73224.29
## 749  59539.29             2   68770.71
## 750  82179.86             2   59539.29
## 751  74252.14             2   82179.86
## 752  73015.00             2   74252.14
## 753  56116.43             2   73015.00
## 754 111885.00             2   56116.43
## 755 131425.14             2  111885.00
## 756 136678.00             2  131425.14
## 757 115531.29             2  136678.00
## 758 118310.86             2  115531.29
## 759 117449.43             2  118310.86
## 760 115193.57             2  117449.43
## 761  61025.43             2  115193.57
## 762  43913.86             2   61025.43
## 763  46099.29             2   43913.86
## 764  44524.86             2   46099.29
## 765  42208.71             2   44524.86
## 766 166486.57             2   42208.71
## 767 171565.29             2  166486.57
## 768 200415.71             2  171565.29
## 769 204498.14             2  200415.71
## 770 197558.86             2  204498.14
## 771 195266.57             2  197558.86
## 772 203144.29             2  195266.57
## 773  85493.71             2  203144.29
## 774  74721.57             2   85493.71
## 775  36232.14             2   74721.57
## 776  40161.71             2   36232.14
## 777  40629.86             2   40161.71
## 778  45663.71             2   40629.86
## 779  39252.29             2   45663.71
## 780  39618.57             2   39252.29
## 781  39438.43             2   39618.57
## 782  44650.71             2   39438.43
## 783  38626.71             2   44650.71
## 784  38280.43             2   38626.71
## 785  44134.14             2   38280.43
## 786  47596.43             2   44134.14
## 787  45598.43             2   47596.43
## 788  42564.29             2   45598.43
## 789  45699.14             2   42564.29
## 790  49553.86             2   45699.14
## 791  50018.43             2   49553.86
## 792  43772.86             2   50018.43
## 793  39235.43             2   43772.86
## 794  39905.00             2   39235.43
## 795  40374.43             2   39905.00
## 796  34230.57             2   40374.43
## 797  34324.14             2   34230.57
## 798  33491.57             2   34324.14
## 799  33366.43             2   33491.57
## 800  46646.86             2   33366.43
## 801  49770.86             2   46646.86
## 802  57339.86             2   49770.86
## 803  59799.14             2   57339.86
## 804  53577.14             2   59799.14
## 805  61775.29             2   53577.14
## 806  70627.86             2   61775.29
## 807  57888.43             2   70627.86
## 808  49960.71             2   57888.43
## 809  42923.71             2   49960.71
## 810  47284.86             2   42923.71
## 811  52284.86             2   47284.86
## 812  50191.00             2   52284.86
## 813  36465.86             2   50191.00
## 814  34525.14             2   36465.86
## 815  43199.14             2   34525.14
## 816  52757.43             2   43199.14
## 817  43200.86             2   52757.43
## 818  36772.29             2   43200.86
## 819  29568.00             2   36772.29
## 820  42362.00             2   29568.00
## 821  42566.29             2   42362.00
## 822  39596.00             2   42566.29
## 823  32925.00             2   39596.00
## 824  43416.57             2   32925.00
## 825  52624.86             2   43416.57
## 826  57733.71             2   52624.86
## 827  54120.57             2   57733.71
## 828  53353.43             2   54120.57
## 829  56286.86             2   53353.43
## 830  60626.86             2   56286.86
## 831  61375.29             2   60626.86
## 832  53710.86             2   61375.29
## 833  55795.57             2   53710.86
## 834  55130.14             2   55795.57
## 835  57700.14             2   55130.14
## 836  61333.14             2   57700.14
## 837  59230.71             2   61333.14
## 838  49195.00             2   59230.71
## 839  55436.43             2   49195.00
## 840  50353.14             2   55436.43
## 841  43194.86             2   50353.14
## 842  47539.71             2   43194.86
## 843  35271.00             2   47539.71
## 844  34774.86             2   35271.00
## 845  48788.71             2   34774.86
## 846  50717.71             2   48788.71
## 847  51727.43             2   50717.71
## 848  51313.14             2   51727.43
## 849  56125.29             2   51313.14
## 850  68503.71             2   56125.29
## 851  62945.00             2   68503.71
## 852  50209.71             2   62945.00
## 853  49436.29             2   50209.71
## 854  55308.00             2   49436.29
## 855  62650.86             2   55308.00
## 856  55683.00             2   62650.86
## 857  49762.14             2   55683.00
## 858  51835.00             2   49762.14
## 859  49806.43             2   51835.00
## 860  52799.57             2   49806.43
## 861  50620.71             2   52799.57
## 862  49192.00             2   50620.71
## 863  66245.86             2   49192.00
## 864  62359.71             2   66245.86
## 865  70816.86             2   62359.71
## 866  84559.71             2   70816.86
## 867  80831.43             2   84559.71
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   710 53691.05 21783.263
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43  63528.57
## [638]  49682.86  47815.00  46546.14  44808.71  42959.57  46023.86  51309.57
## [645]  68447.29  84959.29  81666.29  82700.86  89422.14 104812.71  98812.71
## [652]  64779.86  61862.86  58376.43  59503.57  55429.43  44454.57  47184.00
## [659]  52126.71  51202.00  64437.14  64297.14  64628.57  51413.14  52969.43
## [666]  54135.29  48799.43  41907.86  45382.00  42633.29  46624.71  44051.86
## [673]  35852.86  29737.71  29734.86  32881.71  38298.57  40886.14  38601.86
## [680]  38628.86  39142.57  32666.14  39911.57  39336.29  39678.86  41963.14
## [687]  54220.57  63901.86  73116.00  60863.86  56293.86  52725.00  58625.00
## [694]  47513.00  40300.14  33312.43  29556.71  27816.71  34120.29  32132.57
## [701]  32902.57  39694.14  72501.29  79551.14  99637.71  95424.29  98395.14
## [708] 115594.71 114267.57  88353.29  88750.86  78835.71  75519.14  73202.86
## [715]  53433.29  48165.71  52163.14  49306.86  36846.86  43220.57  38952.29
## [722]  41522.29  39090.00  28452.57  32975.00  33690.71  26405.29  47087.43
## [729]  49660.29  47409.71  53881.71  45189.57  45503.86  54640.14  39131.29
## [736]  35024.14  44755.43  41063.29  42783.29  45952.57  44937.43  40838.43
## [743]  48838.43  43139.14  67134.29  73224.29  68770.71  59539.29  82179.86
## [750]  74252.14  73015.00  56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57  61025.43  43913.86  46099.29  44524.86
## [764]  42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29  85493.71  74721.57  36232.14  40161.71  40629.86  45663.71
## [778]  39252.29  39618.57  39438.43  44650.71  38626.71  38280.43  44134.14
## [785]  47596.43  45598.43  42564.29  45699.14  49553.86  50018.43  43772.86
## [792]  39235.43  39905.00  40374.43  34230.57  34324.14  33491.57  33366.43
## [799]  46646.86  49770.86  57339.86  59799.14  53577.14  61775.29  70627.86
## [806]  57888.43  49960.71  42923.71  47284.86  52284.86  50191.00  36465.86
## [813]  34525.14  43199.14  52757.43  43200.86  36772.29  29568.00  42362.00
## [820]  42566.29  39596.00  32925.00  43416.57  52624.86  57733.71  54120.57
## [827]  53353.43  56286.86  60626.86  61375.29  53710.86  55795.57  55130.14
## [834]  57700.14  61333.14  59230.71  49195.00  55436.43  50353.14  43194.86
## [841]  47539.71  35271.00  34774.86  48788.71  50717.71  51727.43  51313.14
## [848]  56125.29  68503.71  62945.00  50209.71  49436.29  55308.00  62650.86
## [855]  55683.00  49762.14  51835.00  49806.43  52799.57  50620.71  49192.00
## [862]  66245.86  62359.71  70816.86  84559.71  80831.43
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [852] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##            2            3            4            5            6            7 
##   2017.80530   4039.90880   -537.57717   2438.50043  -2968.65351    519.10161 
##            8            9           10           11           12           13 
##  -5655.32556  -1188.28784  -3966.66454   -419.01928  -4940.60487  -1610.93182 
##           14           15           16           17           18           19 
##   -901.24804    376.14026  -3243.83663   -379.17416  -2131.14240   6602.99077 
##           20           21           22           23           24           25 
##  -1529.09831  -1208.39875   1475.39366  -1186.47211    234.64343   1695.13860 
##           26           27           28           29           30           31 
##  -7101.52529    947.00628   8192.30582    419.79375    -12.29847  -2398.87763 
##           32           33           34           35           36           37 
##   1577.50052   4574.13666   1129.37450   2394.03920  -1865.06457   4610.42136 
##           38           39           40           41           42           43 
##   4305.22830  -2273.34196  -2979.71531  -1109.11663 -10740.71732   7288.17369 
##           44           45           46           47           48           49 
##   2557.45231   1367.45857   8105.94701    688.24197   6530.73501   6717.27112 
##           50           51           52           53           54           55 
##  -5878.63424  -4793.15983  -5058.50892  -7928.32053   6128.71548  -4076.52472 
##           56           57           58           59           60           61 
##  -4894.93160   3854.45191    887.28643    -32.45215    141.92125  -4996.67625 
##           62           63           64           65           66           67 
##  18125.72895   3642.80976  -3643.57140   5927.04064   7346.60921  14642.50707 
##           68           69           70           71           72           73 
##   1699.02044 -13207.16861  -1303.28765   4645.94117  -4897.27107  -4402.55772 
##           74           75           76           77           78           79 
## -10496.26172   2466.54702  -5399.40815   1063.48981  -6866.17162    546.20131 
##           80           81           82           83           84           85 
##  -2354.95876  -2694.36719  -3932.51979   -538.44765   2313.87677   3762.42339 
##           86           87           88           89           90           91 
##    477.18264   -484.26341    196.77397   4302.09051  -1162.32067   1151.30285 
##           92           93           94           95           96           97 
##  -2063.91471  -1044.06358    177.66656    274.89650  -7483.88743   2391.04714 
##           98           99          100          101          102          103 
##  -8602.72298  -2941.58975  -4040.41006  -1737.70159  -1262.07407   3180.44044 
##          104          105          106          107          108          109 
##  -2342.00379   2593.90931  -1158.13573    970.63900   2587.41716  -3153.77433 
##          110          111          112          113          114          115 
##  -4722.80709   -850.47295   1903.35026  11693.68856  -1241.12907   2669.71795 
##          116          117          118          119          120          121 
##   4264.25563   3504.49618  -1097.84233  -4714.50078  -3722.90014   2320.53111 
##          122          123          124          125          126          127 
##  -1731.59946   1341.27087   8859.25476    849.10112    132.40492  -2519.42917 
##          128          129          130          131          132          133 
##   2656.49838   7054.18392   1014.80397  -8497.12358   1750.30647   4136.75431 
##          134          135          136          137          138          139 
##  -3162.32557  -1418.59416   -853.06309  -3879.21918   1183.39219   -494.95373 
##          140          141          142          143          144          145 
##  -2913.12018   1718.34319  -1880.66057  -7829.07739   2038.85847  -3479.95990 
##          146          147          148          149          150          151 
##   2101.66017   -257.73820   1022.76696   -359.42256   1352.04656   1186.64897 
##          152          153          154          155          156          157 
##   3356.72207  -4861.29030  -1174.53882  -3235.96410   5956.26731   9746.91620 
##          158          159          160          161          162          163 
##  -3699.65855  -5046.08790   3336.19777    -72.35489   2429.16541  -6177.50437 
##          164          165          166          167          168          169 
##  -7014.79496   3889.10528  17124.67698   3352.50312   -675.36239  -2722.95185 
##          170          171          172          173          174          175 
##  -1380.63451   3314.05736   -506.03791  -8353.42807   2587.67086   4048.06028 
##          176          177          178          179          180          181 
##    346.34409   8470.71972  -9531.38930  -3751.64951 -11023.71198 -11516.24549 
##          182          183          184          185          186          187 
##    961.58318   9018.55170  -1709.03085   5649.31515   6272.15903  12869.95958 
##          188          189          190          191          192          193 
##   8132.91258  -4368.53256   2159.07770  10059.18883  -1961.38285  -2762.37599 
##          194          195          196          197          198          199 
## -10597.43849  -6673.65487    927.45778  -5539.89123 -10098.56036   5088.28248 
##          200          201          202          203          204          205 
##  -3366.48820  -2008.07155  -1098.41733   6200.45979   9581.22430    266.78746 
##          206          207          208          209          210          211 
##   2611.66130   2782.04807   5465.88505  12510.75943  -6020.06279 -11622.08087 
##          212          213          214          215          216          217 
##  -5980.94839 -10895.73692  -5373.41790   1233.56147 -13304.66814  16106.51035 
##          218          219          220          221          222          223 
##   7510.96686   1221.60958  26379.72819  12190.91266   6988.03058  13674.53076 
##          224          225          226          227          228          229 
##  -4276.56416  -2096.38460   3427.43058     10.69731   2401.32292   8662.51354 
##          230          231          232          233          234          235 
##   5486.57232  -2247.74159  -2162.30759   9097.25511 -11841.72401  -7603.72123 
##          236          237          238          239          240          241 
##  -8853.43365 -10405.41751   2782.71580   1054.52961  -8595.63915  -9280.59072 
##          242          243          244          245          246          247 
##   8811.26478  -8058.29976   2200.86969 -10591.07521  -4335.33995   1143.25757 
##          248          249          250          251          252          253 
##    720.05342 -12601.93161   3367.26252   1779.97721   3924.61691   1840.86126 
##          254          255          256          257          258          259 
##  -1458.67475  10840.52959  20567.63557   2856.37632  -4616.07924   3770.43806 
##          260          261          262          263          264          265 
##  -2037.73550   3397.40806  -5194.64039 -11229.06074  -5049.82025   -836.93067 
##          266          267          268          269          270          271 
##  -5503.08133   8468.99855  -4602.60148   3871.67397  -2431.61559   4108.06366 
##          272          273          274          275          276          277 
##    379.01237   6971.24142  -1755.13931  11683.79970  -4944.89196   1371.56295 
##          278          279          280          281          282          283 
##   -728.35464   7496.79781  -5423.70839  -3086.77664 -11609.88713  -2996.07006 
##          284          285          286          287          288          289 
##  18333.54665   7429.40846   2367.28001   -998.37462    539.66850   6032.42667 
##          290          291          292          293          294          295 
##   6507.06166 -19157.42136 -11481.81090  -8438.22409   9366.69925   2754.90732 
##          296          297          298          299          300          301 
##  -1501.64819  27082.66765   9688.88801   4506.95301   9119.20294   2443.96805 
##          302          303          304          305          306          307 
##  -1443.35491   7495.64845 -24705.65936  -3881.00282   -508.17388  -7296.47491 
##          308          309          310          311          312          313 
##  -4280.20381   2635.59897  -9493.96865  -3507.98021  -8455.46554   1316.05009 
##          314          315          316          317          318          319 
##  -3406.17472   1798.59919  -4340.69820  27193.58669  -1067.70428   2950.00890 
##          320          321          322          323          324          325 
##  10481.90481   5215.49061  31997.53037   4655.05103 -21390.37872   1406.77964 
##          326          327          328          329          330          331 
##    728.67562  -6841.55245  -2086.39196 -33609.34482    667.93296  -2517.02636 
##          332          333          334          335          336          337 
##   -300.90380  -3375.75069   3885.22637   -651.35072  -7167.40051  -3313.55389 
##          338          339          340          341          342          343 
##  -2382.85545  -7868.11217   3680.91278  -1559.54900  -1927.19827  -1183.11477 
##          344          345          346          347          348          349 
##    -14.62450    284.88956  -1821.66736  -9649.86072 -13390.85609   2161.05306 
##          350          351          352          353          354          355 
##  -4488.29937  -3818.41595  -6136.97636   1603.09664   1221.19351   2576.03061 
##          356          357          358          359          360          361 
##  -3961.42708   -707.42514    480.08740   6807.00447     40.86611   -278.95213 
##          362          363          364          365          366          367 
##   2339.06419  -3005.35384  -1124.23009  -8988.11557  -4841.13093  -6413.88292 
##          368          369          370          371          372          373 
##  -5133.09086  -7424.27490   4861.42796    190.34314   6930.01834  -7857.79640 
##          374          375          376          377          378          379 
##  -2461.30730  -3582.81386  -2654.73670 -12641.72218   1755.81448 -10797.19172 
##          380          381          382          383          384          385 
##   5561.41952   9169.58690   2916.38434  -2626.22206   1380.69194   6509.24635 
##          386          387          388          389          390          391 
##  11146.63381  -6111.83820  -5654.57720   -432.43370   8286.76712   1505.08753 
##          392          393          394          395          396          397 
##  10905.19283 -10236.37746   2453.52012    382.79545    231.90448   -984.01142 
##          398          399          400          401          402          403 
##   -888.36691 -14808.00746   8264.23198  -1467.19475  -1650.87042   6711.08127 
##          404          405          406          407          408          409 
##  -8228.47417  -1558.21497  -2784.81811  -6059.77073  -3074.81651  -4121.74637 
##          410          411          412          413          414          415 
##  -8946.12832   5973.34769   1449.13908  -7576.72802  -7870.93965  14066.41810 
##          416          417          418          419          420          421 
##   3593.49556   4246.14692  -8304.98589  -4981.76516  -2820.11120   2609.87004 
##          422          423          424          425          426          427 
## -14234.82977  -2961.80001  -9263.38416   2878.03481   6820.44270   6381.29035 
##          428          429          430          431          432          433 
##  -4214.99410  -4335.55293  -4923.69330  -1976.29861  -5896.60893  -6794.47358 
##          434          435          436          437          438          439 
##  -6098.56089  -1527.79290   -987.03945  -5121.05525   2445.42045   4682.33376 
##          440          441          442          443          444          445 
##  -5242.22903  -2330.53963   1405.23431  -4021.76280   2659.71391  -6771.84649 
##          446          447          448          449          450          451 
## -12283.98333  -4645.39694   9518.64758  -2205.03892   4582.91267  -6065.08749 
##          452          453          454          455          456          457 
##  -1300.08780    205.32876   2841.07732 -12469.71084   3209.42680  -6880.23869 
##          458          459          460          461          462          463 
##   6362.40643   2821.58236   2300.85455  -4064.22047   1886.59544   -223.93434 
##          464          465          466          467          468          469 
##   1574.49251   -748.03369   3124.46383  -2878.44853   5576.37129  -7191.81322 
##          470          471          472          473          474          475 
##  -3185.40827  -2414.43297  -4864.72916   2811.58990   7598.71570  -6246.10595 
##          476          477          478          479          480          481 
##   1277.90407  -6391.38417  -3034.90356   1829.56142 -13122.51692  -9905.33626 
##          482          483          484          485          486          487 
##  -1325.60456   -108.16188  -1099.65860  -1484.21269  -9730.26605  10974.78076 
##          488          489          490          491          492          493 
##   6066.61957   7224.38072  -5661.54982   5160.88634   9065.87513   5793.96931 
##          494          495          496          497          498          499 
## -13751.91137 -10791.92478  -3628.91196  -1283.49508   -701.04672  -7803.93317 
##          500          501          502          503          504          505 
##    456.71994   4126.45584   5328.18197    457.87028   -125.37037  -7445.95478 
##          506          507          508          509          510          511 
##    387.36219  -5235.30941   1660.64135  -1478.07092  -8337.83949   -757.51964 
##          512          513          514          515          516          517 
##  -2833.11125   -742.38240   1174.05928  -9663.05801  -7905.92466  24164.34424 
##          518          519          520          521          522          523 
##   9612.28601   5631.86591  -5601.35928   2554.27773  16767.24824  11165.82842 
##          524          525          526          527          528          529 
## -24488.93645  -5307.04424  -3960.38839   4358.91053   -586.04869 -11330.00236 
##          530          531          532          533          534          535 
##   4202.86646  13703.66442  -5231.67683   4138.06322   5304.91990  -2057.75025 
##          536          537          538          539          540          541 
##  -4798.50402  -7312.60743  -2312.36361   8118.28396   -106.04249  -8373.55916 
##          542          543          544          545          546          547 
##   1613.36907   -809.10406    158.54704 -11240.27092 -11233.81208   1902.38849 
##          548          549          550          551          552          553 
##   6851.48072  -1499.04125    656.88680  -7906.73680   8402.39859    710.34415 
##          554          555          556          557          558          559 
## -12145.74693   8999.23062   8457.66339   -133.56017   4620.18510  -3823.74789 
##          560          561          562          563          564          565 
##  13872.93038  21215.00885  -6820.19237 -10001.55132   6495.94488    -71.20415 
##          566          567          568          569          570          571 
##   3163.16525  -7674.11105 -17571.46911   6465.58648   6215.70855   1663.55284 
##          572          573          574          575          576          577 
##   2856.57432   1521.39719  -2415.33015  14478.31245  -9935.47037  -6492.72545 
##          578          579          580          581          582          583 
##   8487.37329   2606.22263  -6803.73909   7277.13194  -4054.76821  -3013.42967 
##          584          585          586          587          588          589 
##  15477.09032 -14776.19253   8205.16203   -179.33831  -6458.14409   -974.72504 
##          590          591          592          593          594          595 
##     36.23428 -10867.99515   1619.43906  -7329.30063   2905.57987   8690.92104 
##          596          597          598          599          600          601 
##  -7707.97187   5669.05803   2531.08316   6642.60872  -3425.40062   5927.66679 
##          602          603          604          605          606          607 
##  -8538.73073   2046.15935   1054.56434   2920.38778   1268.98761    169.13309 
##          608          609          610          611          612          613 
##  -6036.98329   7870.40329  -1413.10967  -2795.89009  -3663.32694  -8424.59955 
##          614          615          616          617          618          619 
##  11790.03951   4728.04644  -9536.72060  11437.04255   5824.08602  -5814.09259 
##          620          621          622          623          624          625 
##  26142.15701 -13119.64190  -7021.04864   2943.69508  -4378.84082 -10794.99662 
##          626          627          628          629          630          631 
##  11128.43933 -21838.79199  -2552.03791   8541.03847  10971.24395  -1751.48471 
##          632          633          634          635          636          637 
##  33091.64027  -6876.11794   5458.46965   5131.67642  -2542.87140  -5602.96620 
##          638          639          640          641          642          643 
##  -2173.97769 -12653.48277  -2424.45463  -2061.38054  -2690.21919  -3021.38517 
##          644          645          646          647          648          649 
##   1658.48126   4266.95463  16786.58436  18325.50609    606.10918   4517.74729 
##          650          651          652          653          654          655 
##  10335.13656  19853.37663    406.76252 -28383.94458  -1566.72068  -2504.59064 
##          656          657          658          659          660          661 
##   1668.61583  -3390.30235 -10805.61481   1512.45507   4070.49034  -1172.63227 
##          662          663          664          665          666          667 
##  12870.42576   1166.99165   1620.73706 -11884.25789   1218.23772   1024.38099 
##          668          669          670          671          672          673 
##  -5330.07583  -7559.75330   1935.49813  -3848.54584   2544.41151  -3515.72354 
##          674          675          676          677          678          679 
##  -9466.83967  -8418.58450  -3078.69225     70.66116   2738.13542    593.04386 
##          680          681          682          683          684          685 
##  -3951.98146  -1929.22005  -1439.09544  -8364.35191   4539.47838  -2366.07795 
##          686          687          688          689          690          691 
##  -1520.88418    464.09973  10725.76689   9697.83933  10453.52347  -9848.93928 
##          692          693          694          695          696          697 
##  -3714.34409  -3290.43028   5727.65049 -10539.13038  -8043.52565  -8729.42671 
##          698          699          700          701          702          703 
##  -6380.03321  -4838.69691   2985.09803  -4509.99413  -2003.34471   4115.48413 
##          704          705          706          707          708          709 
##  30988.88758   9375.41719  23302.58713   1539.68836   8191.78296  22795.74124 
##          710          711          712          713          714          715 
##   6441.47604 -18313.29601   4725.37107  -5537.12664   -190.92031    390.45482 
##          716          717          718          719          720          721 
## -17355.39707  -5350.45856   3249.20331  -3099.60246 -13064.08942   4195.82312 
##          722          723          724          725          726          727 
##  -5641.12370    658.04199  -4019.63132 -12531.99212   1284.26922  -1951.22468 
##          728          729          730          731          732          733 
##  -9861.96686  17185.39428   1688.43542  -2810.01987   5628.28564  -8718.38972 
##          734          735          736          737          738          739 
##   -809.85117   8051.84573 -15439.30817  -5996.49167   7323.17056  -4871.11560 
##          740          741          742          743          744          745 
##     74.67887   1741.21491  -2042.90649  -5254.98463   6326.27754  -6362.54156 
##          746          747          748          749          750          751 
##  22612.01977   7737.66326  -2036.69046  -7377.07076  23328.92294  -4379.67008 
##          752          753          754          755          756          757 
##   1309.56502 -14508.12499  56024.58758  26840.19395  15020.99099 -10715.10085 
##          758          759          760          761          762          763 
##  10540.17877   7250.26179   5747.02761 -46450.19164 -16235.50770    900.15835 
##          764          765          766          767          768          769 
##  -2583.66097  -3524.23869 122777.21318  19275.39850  43688.59669  22564.64605 
##          770          771          772          773          774          775 
##  12058.57648  15829.08692  25709.55214 -98823.71266  -6805.53090 -35883.42795 
##          776          777          778          779          780          781 
##   1674.03673  -1291.05425   3333.79038  -7475.67727  -1507.77979  -2007.94343 
##          782          783          784          785          786          787 
##   3361.73160  -7216.19903  -2299.36610   3856.89513   2204.83941  -2818.13079 
##          788          789          790          791          792          793 
##  -4106.63768   1679.12481   2794.94051   -108.31983  -6759.78345  -5840.50827 
##          794          795          796          797          798          799 
##  -1206.62328  -1322.19369  -7876.18667  -2414.77841  -3329.10242  -2726.83455 
##          800          801          802          803          804          805 
##  10662.93030   2183.93043   7023.51765   2869.83109  -5500.82636   8133.42608 
##          806          807          808          809          810          811 
##   9823.34836 -10650.49815  -7447.87977  -7558.50181   2950.80935   4140.51515 
##          812          813          814          815          816          817 
##  -2321.80036 -14217.55767  -4166.72893   6202.85699   8182.74113  -9724.82494 
##          818          819          820          821          822          823 
##  -7803.89949  -9391.59587   9696.72857  -1276.99699  -4425.76543  -8501.65153 
##          824          825          826          827          828          829 
##   7818.31705   7860.20417   4923.85875  -3152.85005   -763.22008   2840.45482 
##          830          831          832          833          834          835 
##   4617.54270   1574.14941  -6744.17497   2036.88674   -449.93934   2701.44006 
##          836          837          838          839          840          841 
##   4089.05246  -1187.49796  -9386.33791   5623.21067  -4913.15922  -7630.22053 
##          842          843          844          845          846          847 
##   2968.77123 -13096.00856  -2873.07790  11574.25512   1259.46483    583.82787 
##          848          849          850          851          852          853 
##   -712.63680   4461.46403  12635.56346  -3738.08080 -11616.76413  -1263.47961 
##          854          855          856          857          858          859 
##   5283.97278   7496.76204  -5886.48825  -5719.58662   1526.27410  -2313.33535 
##          860          861          862          863          864          865 
##   2452.15347  -2341.78766  -1866.85261  16435.26031  -2350.69553   9501.73798 
##          866          867 
##  15855.65983    120.35427 
## 
## $fitted.values
##         2         3         4         5         6         7         8         9 
##  17251.48  20099.09  24353.72  24071.64  26425.37  23757.61  24474.04  19705.43 
##        10        11        12        13        14        15        16        17 
##  19441.95  16784.30  17561.89  14290.79  14341.96  15006.72  16703.55  15023.32 
##        18        19        20        21        22        23        24        25 
##  16058.14  15431.58  22515.10  21598.97  21078.75  22969.04  22294.93  22947.58 
##        26        27        28        29        30        31        32        33 
##  24793.81  18721.28  20447.69  28286.21  28343.87  28016.73  25645.79  27048.43 
##        34        35        36        37        38        39        40        41 
##  30892.05  31240.53  32649.92  30160.15  34137.77  37346.34  34402.00  31212.40 
##        42        43        44        45        46        47        48        49 
##  30060.00  20638.11  28157.98  30594.83  31684.20  38523.33  38017.84  42680.73 
##        50        51        52        53        54        55        56        57 
##  46917.63  39614.45  34182.08  29204.03  22347.43  28638.38  25218.50  21515.55 
##        58        59        60        61        62        63        64        65 
##  25924.57  27184.31  27481.36  27893.25  23763.56  40357.33  42201.57  37446.82 
##        66        67        68        69        70        71        72        73 
##  41654.39  46570.78  57240.55  55254.03  40495.00  38000.49  41018.84  35318.13 
##        74        75        76        77        78        79        80        81 
##  30769.69  21471.74  24673.69  20598.80  22685.17  17579.94  19595.67  18822.08 
##        82        83        84        85        86        87        88        89 
##  17849.66  15918.30  17196.27  20804.86  25223.25  26213.26  26238.23  26855.05 
##        90        91        92        93        94        95        96        97 
##  30980.75  29811.13  30810.63  28874.78  28074.48  28442.67  28849.32  22425.81 
##        98        99       100       101       102       103       104       105 
##  25441.29  18470.73  17326.70  15367.13  15666.93  16344.42  20817.72  19901.09 
##       106       107       108       109       110       111       112       113 
##  23412.71  23202.65  24879.01  27756.20  25253.95  21696.90  21972.36  24619.03 
##       114       115       116       117       118       119       120       121 
##  35485.13  33677.71  35515.46  38514.22  40470.41  38158.50  32978.76  29319.61 
##       122       123       124       125       126       127       128       129 
##  31402.74  29682.44  30864.17  38465.04  38107.45  37168.86  34031.93  35813.39 
##       130       131       132       133       134       135       136       137 
##  41212.05  40652.27  31852.69  33117.67  36307.90  32718.02  31105.06  30189.93 
##       138       139       140       141       142       143       144       145 
##  26746.46  28161.10  27930.69  25616.66  27641.37  26265.93  19867.14  22898.10 
##       146       147       148       149       150       151       152       153 
##  20724.48  23702.02  24242.09  25832.71  26014.81  27669.21  28970.14  32002.72 
##       154       155       156       157       158       159       160       161 
##  27472.25  26735.11  24290.02  30184.94  41720.09  40050.09  37414.66  42435.64 
##       162       163       164       165       166       167       168       169 
##  43844.41  47260.79  42726.08  38032.61  43458.61  59763.07  61975.51  60389.38 
##       170       171       172       173       174       175       176       177 
##  57214.63  55613.66  58316.61  57340.57  49631.61  52455.51  56198.66  56234.85 
##       178       179       180       181       182       183       184       185 
##  63364.68  53865.65  50616.14  41423.53  32961.70  36470.45  46575.32  46031.26 
##       186       187       188       189       190       191       192       193 
##  51984.84  57730.61  68515.09  73798.68  67492.49  67685.95  74757.24  70433.09 
##       194       195       196       197       198       199       200       201 
##  65955.30  55197.65  49226.97  50651.46  46245.56  38413.29  44838.92  43066.07 
##       202       203       204       205       206       207       208       209 
##  42703.99  43182.40  49977.35  58867.78  58497.34  60222.38  61878.40  65670.10 
##       210       211       212       213       214       215       216       217 
##  75137.92  67219.65  55407.09  50015.17  41010.28  37967.58  41081.67  31100.49 
##       218       219       220       221       222       223       224       225 
##  48076.32  55398.10  56300.13  79068.66  86564.68  88568.18  96160.56  87110.24 
##       226       227       228       229       230       231       232       233 
##  81107.86  80689.73  77339.25  76500.63  81238.28  82602.74  77037.45  72249.74 
##       234       235       236       237       238       239       240       241 
##  77904.15  64550.15  56585.58  48535.13  40145.57  44338.04  46491.07  39940.88 
##       242       243       244       245       246       247       248       249 
##  33619.59  43903.44  38149.56  42085.79  34348.63  33054.31  36710.09  39534.36 
##       250       251       252       253       254       255       256       257 
##  30362.59  36301.45  40103.38  45298.85  48017.53  47510.04  57812.36  75311.91 
##       258       259       260       261       262       263       264       265 
##  75126.94  68436.70  69918.74  66139.02  67585.35  61342.20  50615.39  46642.22 
##       266       267       268       269       270       271       272       273 
##  46851.65  42957.86  51763.17  48035.75  52183.04  50299.36  54367.27  54663.33 
##       274       275       276       277       278       279       280       281 
##  60681.57  58315.49  67989.75  61913.72  62123.78  60472.63  66216.28  59945.92 
##       282       283       284       285       286       287       288       289 
##  56509.32  46060.21  44456.74  61691.31  67222.15  67631.66  65048.90  64136.14 
##       290       291       292       293       294       295       296       297 
##  68137.65  72048.42  53042.38  43143.08  37153.30  47476.09  50718.36  49832.19 
##       298       299       300       301       302       303       304       305 
##  74031.83  79978.05  80645.80  85258.89  83457.21  78486.78  81954.09  56849.43 
##       306       307       308       309       310       311       312       313 
##  53110.03  52789.76  46579.06  43788.12  47391.97  39943.12  38665.04  33225.81 
##       314       315       316       317       318       319       320       321 
##  37010.89  36192.12  40024.13  38008.27  63798.28  61639.13  63262.95  71262.22 
##       322       323       324       325       326       327       328       329 
##  73649.90  99135.23  97512.66  73339.36  72137.04  70494.12  62444.68  59566.49 
##       330       331       332       333       334       335       336       337 
##  29510.50  33198.60  33638.19  35958.46  35299.20  41067.07  42142.83  37389.70 
##       338       339       340       341       342       343       344       345 
##  36604.00  36730.68  32048.94  38048.83  38712.34  38970.83  39846.77  41632.97 
##       346       347       348       349       350       351       352       353 
##  43455.24  43206.86  36150.43  26716.80  32062.30  30923.13  30513.12  28129.19 
##       354       355       356       357       358       359       360       361 
##  32808.81  36563.68  41028.00  39216.71  40477.20  42616.00  50012.42  50563.09 
##       362       363       364       365       366       367       368       369 
##  50764.79  53228.35  50711.37  50155.83  42799.85  39996.17  36172.52  33950.85 
##       370       371       372       373       374       375       376       377 
##  30008.00  37297.09  39584.41  47471.22  41441.88  40888.96  39426.02  38958.72 
##       378       379       380       381       382       383       384       385 
##  29824.90  34423.76  27474.29  35694.98  46029.76  49595.79  47868.88  49860.90 
##       386       387       388       389       390       391       392       393 
##  56082.08  65569.12  58779.29  53246.58  52975.23  60356.06  60879.52  69549.66 
##       394       395       396       397       398       399       400       401 
##  58653.48  60220.63  59780.67  59264.44  57751.08  56512.44  43268.77  51855.91 
##       402       403       404       405       406       407       408       409 
##  50856.16  49822.20  56224.62  48765.79  48076.82  46403.20  42079.67  40910.17 
##       410       411       412       413       414       415       416       417 
##  38973.70  33066.80  40941.00  43867.87  38539.23  33626.58  48500.93  52346.42 
##       418       419       420       421       422       423       424       425 
##  56276.41  48744.19  45066.83  43742.56  47329.69  35746.66  35475.81  29733.54 
##       426       427       428       429       430       431       432       433 
##  35324.41  43653.57  50546.99  47311.84  44379.98  41304.58  41192.75  37669.90 
##       434       435       436       437       438       439       440       441 
##  33807.56  31041.08  32617.47  34467.20  32471.44  37338.52  43545.23  40296.97 
##       442       443       444       445       446       447       448       449 
##  40002.91  43009.91  40895.57  44885.85  40131.84  31162.40  29999.64  41358.75 
##       450       451       452       453       454       455       456       457 
##  41040.23  46692.52  42327.80  42677.53  44298.35  48017.28  37889.57  42739.81 
##       458       459       460       461       462       463       464       465 
##  38162.17  45732.70  49253.43  51874.51  48603.40  50944.65  51146.22  52893.61 
##       466       467       468       469       470       471       472       473 
##  52391.11  55335.45  52663.20  57715.38  50973.98  48584.43  47170.30  43793.98 
##       474       475       476       477       478       479       480       481 
##  47550.86  55015.68  49441.52  51145.10  45932.90  44311.58  47145.09  36557.19 
##       482       483       484       485       486       487       488       489 
##  30117.46  31987.16  34684.37  36174.64  37140.69  30780.22  43312.95  49974.48 
##       490       491       492       493       494       495       496       497 
##  56806.12  51516.54  56350.55  63985.74  67797.91  54051.50  44627.48  42652.07 
##       498       499       500       501       502       503       504       505 
##  42975.33  43766.65  38252.28  40651.69  45954.25  51636.99  52346.80  52457.38 
##       506       507       508       509       510       511       512       513 
##  46158.07  47498.31  43756.79  46512.79  46178.41  39892.95  41024.25  40199.24 
##       514       515       516       517       518       519       520       521 
##  41305.08  43945.63  36784.35  32062.80  55957.14  64119.42  67773.07  61150.87 
##       522       523       524       525       526       527       528       529 
##  62490.61  76078.89  83056.94  58002.33  52871.39  49565.09  53944.91  53451.15 
##       530       531       532       533       534       535       536       537 
##  43632.85  48625.62  61288.53  55808.37  59206.65  63195.18  60247.22  55277.04 
##       538       539       540       541       542       543       544       545 
##  48738.08  47393.72  55332.33  55082.70  47641.35  49865.39  49692.02  50385.99 
##       546       547       548       549       550       551       552       553 
##  41033.24  32867.47  37210.09  45328.18  45125.11  46831.31  40840.03  49854.66 
##       554       555       556       557       558       559       560       561 
##  51010.18  40787.48  50330.19  58194.42  57559.24  61157.61  56924.07  68686.71 
##       562       563       564       565       566       567       568       569 
##  85378.34  75467.55  64029.06  68449.06  66573.12  67759.97  59328.47  43314.70 
##       570       571       572       573       574       575       576       577 
##  50324.58  56230.73  57413.71  59489.60  60136.76  57262.69  69511.47  58883.01 
##       578       579       580       581       582       583       584       585 
##  52604.91  60207.78  61712.02  54804.87  61072.48  56647.86  53691.91  67264.34 
##       586       587       588       589       590       591       592       593 
##  52690.41  60035.91  59128.14  52849.30  52154.34  52430.42  43144.70  45942.01 
##       594       595       596       597       598       599       600       601 
##  40567.56  44814.08  53578.83  46908.94  52768.92  55147.11  60817.11  56974.62 
##       602       603       604       605       606       607       608       609 
##  61789.16  53356.41  55236.72  56013.18  58321.73  58895.87  58436.55  52613.03 
##       610       611       612       613       614       615       616       617 
##  59675.82  57735.60  54832.33  51537.89  44499.67  56011.81  59899.86  50833.81 
##       618       619       620       621       622       623       624       625 
##  61237.49  65423.09  58911.84  81142.93  66263.33  58591.45  60594.70  55947.28 
##       626       627       628       629       630       631       632       633 
##  46281.13  56990.22  37543.47  37403.68  46973.47  57457.77  55502.07  84235.55 
##       634       635       636       637       638       639       640       641 
##  74420.24  76621.32  78258.87  72984.39  65702.55  62336.34  50239.45  48607.52 
##       642       643       644       645       646       647       648       649 
##  47498.93  45980.96  44365.38  47042.62  51660.70  66633.78  81060.18  78183.11 
##       650       651       652       653       654       655       656       657 
##  79087.01  84959.34  98405.95  93163.80  63429.58  60881.02  57834.96  58819.73 
##       658       659       660       661       662       663       664       665 
##  55260.19  45671.54  48056.22  52374.63  51566.72  63130.15  63007.83  63297.40 
##       666       667       668       669       670       671       672       673 
##  51751.19  53110.90  54129.50  49467.61  43446.50  46481.83  44080.30  47567.58 
##       674       675       676       677       678       679       680       681 
##  45319.70  38156.30  32813.55  32811.05  35560.44  40293.10  42553.84  40558.08 
##       682       683       684       685       686       687       688       689 
##  40581.67  41030.49  35372.09  41702.36  41199.74  41499.04  43494.80  54204.02 
##       690       691       692       693       694       695       696       697 
##  62662.48  70712.80  60008.20  56015.43  52897.35  58052.13  48343.67  42041.86 
##       698       699       700       701       702       703       704       705 
##  35936.75  32655.41  31135.19  36642.57  34905.92  35578.66  41512.40  70175.73 
##       706       707       708       709       710       711       712       713 
##  76335.13  93884.60  90203.36  92798.97 107826.10 106666.58  84025.49  84372.84 
##       714       715       716       717       718       719       720       721 
##  75710.06  72812.40  70788.68  53516.17  48913.94  52406.46  49910.95  39024.75 
##       722       723       724       725       726       727       728       729 
##  44593.41  40864.24  43109.63  40984.56  31690.73  35641.94  36267.25  29902.03 
##       730       731       732       733       734       735       736       737 
##  47971.85  50219.73  48253.43  53907.96  46313.71  46588.30  54570.59  41020.63 
##       738       739       740       741       742       743       744       745 
##  37432.26  45934.40  42708.61  44211.36  46980.34  46093.41  42512.15  49501.68 
##       746       747       748       749       750       751       752       753 
##  44522.27  65486.62  70807.40  66916.36  58850.93  78631.81  71705.43  70624.55 
##       754       755       756       757       758       759       760       761 
##  55860.41 104584.95 121657.01 126246.39 107770.68 110199.17 109446.54 107475.62 
##       762       763       764       765       766       767       768       769 
##  60149.36  45199.13  47108.52  45732.95  43709.36 152289.89 156727.12 181933.50 
##       770       771       772       773       774       775       776       777 
## 185500.28 179437.48 177434.73 184317.43  81527.10  72115.57  38487.68  41920.91 
##       778       779       780       781       782       783       784       785 
##  42329.92  46727.96  41126.35  41446.37  41288.98  45842.91  40579.79  40277.25 
##       786       787       788       789       790       791       792       793 
##  45391.59  48416.56  46670.92  44020.02  46758.92  50126.75  50532.64  45075.94 
##       794       795       796       797       798       799       800       801 
##  41111.62  41696.62  42106.76  36738.92  36820.67  36093.26  35983.93  47586.93 
##       802       803       804       805       806       807       808       809 
##  50316.34  56929.31  59077.97  53641.86  60804.51  68538.93  57408.59  50482.22 
##       810       811       812       813       814       815       816       817 
##  44334.05  48144.34  52512.80  50683.41  38691.87  36996.29  44574.69  52925.68 
##       818       819       820       821       822       823       824       825 
##  44576.19  38959.60  32665.27  43843.28  44021.77  41426.65  35598.25  44764.65 
##       826       827       828       829       830       831       832       833 
##  52809.86  57273.42  54116.65  53446.40  56009.31  59801.14  60455.03  53758.68 
##       834       835       836       837       838       839       840       841 
##  55580.08  54998.70  57244.09  60418.21  58581.34  49813.22  55266.30  50825.08 
##       842       843       844       845       846       847       848       849 
##  44570.94  48367.01  37647.94  37214.46  49458.25  51143.60  52025.78  51663.82 
##       850       851       852       853       854       855       856       857 
##  55868.15  66683.08  61826.48  50699.77  50024.03  55154.10  61569.49  55481.73 
##       858       859       860       861       862       863       864       865 
##  50308.73  52119.76  50347.42  52962.50  51058.85  49810.60  64710.41  61315.12 
##       866       867 
##  68704.05  80711.07 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8088
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original      bias    std. error
## t1*    5.421628   0.7664183    3.986956
## t2* 2754.842631 160.1366960  883.779027
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    1.270346       5.430432   13.74066
## 2    lag_depvar 1691.693925    2791.754064 4551.28425

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
    dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03")))

# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
  Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
  Data = as.numeric(mstl_data_autplt[, "Data"]),
  Trend = as.numeric(mstl_data_autplt[, "Trend"]),
  Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)

# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
  pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")

# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
  geom_line() +
  theme_bw() + 
  labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
  scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
  facet_wrap(~ Component, scales = "free_y", ncol = 1) +
  theme(strip.text = element_text(size = 12),
        axis.text.x = element_text(angle = 90, hjust = 1))

ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
                  gasto=="aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  gasto=="Tina"~"Electrodomésticos/mantención casa",
                  gasto=="Nexium"~"Farmacia",
                  gasto=="donaciones"~"Donaciones/regalos",
                  gasto=="Regalo chocolates"~"Donaciones/regalos",
                  gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
                  gasto=="Chromecast"~"Electrodomésticos/mantención casa",
                  gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
                  gasto=="Vacuna Influenza"~"Farmacia",
                  gasto=="Easy"~"Electrodomésticos/mantención casa",
                  gasto=="Sopapo"~"Electrodomésticos/mantención casa",
                  gasto=="filtro agua"~"Electrodomésticos/mantención casa",
                  gasto=="ropa tami"~"Donaciones/regalos",
                  gasto=="yaz"~"Farmacia",
                  gasto=="Yaz"~"Farmacia",
                  gasto=="Remedio"~"Farmacia",
                  gasto=="Entel"~"VTR",
                  gasto=="Kerosen"~"Gas/Bencina",
                  gasto=="Parafina"~"Gas/Bencina",
                  gasto=="Plata basurero"~"Donaciones/regalos",
                  gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
                  gasto=="Wild Protein"~"Comida",
                  gasto=="Granola Wild Foods"~"Comida",
                  gasto=="uber"~"Transporte",
                  gasto=="Uber Reñaca"~"Transporte",
                  gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
                  gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
                  gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
                  gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
                  gasto=="Reloj"~"Electrodomésticos/mantención casa",
                  gasto=="Arreglo"~"Electrodomésticos/mantención casa",
                  gasto=="Pan Pepperino"~"Comida",
                  gasto=="Cookidoo"~"Comida",
                  gasto=="remedios"~"Farmacia",
                  gasto=="Bendina Reñaca"~"Gas/Bencina",
                  gasto=="Bencina Reñaca"~"Gas/Bencina",
                  gasto=="Vacunas Influenza"~"Farmacia",
                  gasto=="Remedios"~"Farmacia",
                  gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
                  #2024
                  gasto=="cartero"~"Correo",
                  gasto=="correo"~"Correo",
                  gasto=="Gaviscón y Paracetamol"~"Farmacia",
                  gasto=="Regalo Matri Cony"~"Donaciones/regalos",
                  gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
                  gasto=="Aporte Basureros"~"Donaciones/regalos",
                  gasto=="donación"~"Donaciones/regalos",
                  gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
                  gasto=="basureros"~"Donaciones/regalos",
                  gasto=="Microondas regalo"~"Donaciones/regalos",
                  gasto=="Cruz Verde"~"Farmacia",
                  gasto=="Remedios Covid"~"Farmacia",      
                  gasto=="nacho"~"Electrodomésticos/mantención casa",
                  gasto=="Jardinero"~"Electrodomésticos/mantención casa",
                  gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
                  gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",      
                  gasto=="Uber cumple papá"~"Transporte",
                  gasto=="Uber"~"Transporte",
                  gasto=="Uber Matri Cony"~"Transporte",
                  gasto=="Bencina + tag"~"Gas/Bencina",
                  gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
                  gasto=="Bencina + peajes Maite"~"Gas/Bencina",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Crunchyroll"~"Netflix",
                  gasto=="Incoludido"~"Enceres",
                  gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
                  gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
                  gasto=="Brussels"~"Comida",
                  gasto=="Tres toques"~"Enceres",
                  gasto=="Transferencia"~"Otros",
                  gasto=="prestamo"~"Otros",
                  gasto=="Préstamo Andrés"~"Otros",
                  gasto=="mouse"~"Otros",
                  gasto=="lamina"~"Otros",
      T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_25<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2025",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_24<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2024",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2020",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_25 %>% 
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))
Item 2025 2024 2023 2022 2021 2020
Agua 6.382333 6.993667 5.195333 5.410333 5.849167 9.93775
Comida 234.753000 326.890000 366.009167 312.386750 317.896583 392.93367
Comunicaciones 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000
Electricidad 49.655556 83.582750 38.104750 47.072333 29.523000 20.60458
Enceres 2.964444 23.989000 18.259750 24.219750 14.801167 39.01200
Farmacia 0.000000 0.000000 10.704083 2.835000 13.996083 14.03675
Gas/Bencina 38.538889 44.292667 42.636000 45.575000 13.583667 17.25833
Diosi 20.275444 33.319583 55.804250 31.180667 52.687833 37.12133
donaciones/regalos 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000
Electrodomésticos/ Mantención casa 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000
VTR 17.108889 18.326667 12.829167 25.156667 19.086917 19.11375
Netflix 0.000000 1.391417 8.713833 7.151583 7.028750 8.24725
Otros 0.000000 76.164000 5.481667 5.000000 0.000000 0.00000
Total 369.678556 614.949750 563.738000 505.988083 474.453167 558.26542
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")

tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  54 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2838, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)
## Warning in bats(as.numeric(y), use.box.cox = use.box.cox, use.trend =
## use.trend, : optim() did not converge.
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)
# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))
## API key has been set for the current session.
try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))
## API key has been set for the current session.
# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
    dplyr::rename(ds = date3, y = value) %>%
    dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
    dplyr::mutate(unique_id = "serie_1")%>%
    dplyr::select(unique_id, ds, y)

# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
  uf_timegpt,
  h = 298,               # 298 días a pronosticar
  freq = "D",            # Frecuencia diaria
  add_history = TRUE,     # Incluir datos históricos en el output
  level = c(80,95),
  model=  "timegpt-1-long-horizon", 
  clean_ex_first = TRUE
)
## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.
# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>% 
    mutate(ds = as.Date(ds))

timegpt_fcst <- timegpt_fcst %>% 
    mutate(ds = as.Date(ds))

# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
    uf_timegpt %>% mutate(type = "Histórico"),
    timegpt_fcst %>% mutate(type = "Pronóstico")
)

# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
    # Intervalo de confianza del 95%
    geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`), 
                fill = "#4B9CD3", alpha = 0.2) +
    # Intervalo de confianza del 80%
    geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`), 
                fill = "#4B9CD3", alpha = 0.3) +
    # Línea histórica
    geom_line(data = filter(full_data, type == "Histórico"), 
              aes(color = "Histórico"), size = 1) +
    # Línea de pronóstico
    geom_line(data = filter(full_data, type == "Pronóstico"), 
              aes(color = "Pronóstico"), size = 1) +
    # Línea vertical separadora
    geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds), 
               linetype = "dashed", color = "red", size = 0.8) +
    # Configuración del eje x
    scale_x_date(
        date_breaks = "3 months",  # Reduce la frecuencia de las etiquetas
        date_labels = "%b %Y",  # Formato de etiquetas (mes y año)
    ) +
    # Configuración del eje y
    scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
    # Configuración de colores
    scale_color_manual(
        name = "Leyenda",
        values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
    ) +
    # Títulos y subtítulos
    labs(
        title = "Pronóstico de Serie Temporal con TimeGPT",
        subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
        x = "Fecha",
        y = "Valor",
        color = "Leyenda"
    ) +
    # Tema y estilos
    theme_minimal() +
    theme(
        axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
        axis.title.x = element_text(size = 10),
        axis.title.y = element_text(size = 10),
        legend.position = "bottom",
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank()
    )
## Warning: Removed 2838 rows containing missing values or values outside the scale range
## (`geom_ribbon()`).
## Removed 2838 rows containing missing values or values outside the scale range
## (`geom_ribbon()`).
## Warning: Removed 2838 rows containing missing values or values outside the scale range
## (`geom_line()`).

library(prophet)
## Warning: package 'prophet' was built under R version 4.4.3
## Loading required package: Rcpp
## Warning: package 'Rcpp' was built under R version 4.4.3
## Loading required package: rlang
## Warning: package 'rlang' was built under R version 4.4.3
## 
## Attaching package: 'rlang'
## The following objects are masked from 'package:purrr':
## 
##     %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
##     flatten_raw, invoke, splice
## The following object is masked from 'package:sparklyr':
## 
##     invoke
## The following object is masked from 'package:data.table':
## 
##     :=
  model <- prophet(
  cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
  # Trend flexibility
  growth = "linear",
  changepoint.prior.scale = 0.05,  # Reduced for smoother trend
  n.changepoints = 50,  # Increased from default 25
  
  # Seasonality
  yearly.seasonality = TRUE,
  weekly.seasonality = TRUE,
  daily.seasonality = FALSE,  # Disabled for daily data
  seasonality.mode = "additive",
  seasonality.prior.scale = 15,  # Increased to capture stronger seasonality
  
  # Holidays (if applicable)
  # holidays = generated_holidays  # Create with add_country_holidays()
  
  # Uncertainty intervals
  interval.width = 0.95,
  uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)
## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistent
forecast$ds <- as.Date(forecast$ds)

ggplot(forecast, aes(x = ds, y = yhat)) +
  geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper), 
              fill = "#9ecae1", alpha = 0.4) +
  geom_line(color = "#08519c", linewidth = 0.8) +
  geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
  scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
  scale_y_continuous(labels = scales::comma) +
  labs(title = "Valores predichos (95%IC)",
      # subtitle = "March 10, 2025 - May 7, 2025",
       x = "Fecha",
       y = "Valor",
      # caption = "Source: Prophet Forecast Model"
      ) +
  theme_minimal() +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    plot.subtitle = element_text(color = "gray50"),
    axis.text.x = element_text(angle = 45, hjust = 1),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    plot.caption = element_text(color = "gray30")
  )

La proyección de la UF a 298 días más 2025-10-09 00:04:58 sería de: 26.544 pesos// Percentil 95% más alto proyectado: 35.016,65

Según TimeGPT: La proyección de la UF a 298 días más 2026-08-03 sería de: 39.999,44 pesos// Percentil 80% más alto proyectado: 42.030,93 pesos// Percentil 95% más alto proyectado: 42.301,02

Según prophet: La proyección de la UF a 298 días más 2026-08-03 sería de: 42.500 pesos// Percentil 95% más alto proyectado: 49.080

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## i Please use tidy evaluation idioms with `aes()`.
## i See also `vignette("ggplot2-in-packages")` for more information.
## i The deprecated feature was likely used in the ggfortify package.
##   Please report the issue at <https://github.com/sinhrks/ggfortify/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 26163.23 26324.26
Lo.80 26294.43 26488.59
Point.Forecast 26544.08 26799.01
Hi.80 31372.77 32157.70
Hi.95 34274.97 34994.42


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1       mean
##       0.4461  1030.7989
## s.e.  0.1017    38.8318
## 
## sigma^2 = 38578:  log likelihood = -535.03
## AIC=1076.06   AICc=1076.38   BIC=1083.21
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1  intercept     xreg
##       0.4530   591.1895  13.4662
## s.e.  0.1031   309.9789   9.4105
## 
## sigma^2 = 38045:  log likelihood = -533.96
## AIC=1075.93   AICc=1076.46   BIC=1085.45
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 624.0144 600.6350 590.2443
Lo.80 772.4413 749.5237 680.5277
Point.Forecast 1052.8264 1030.7811 890.4046
Hi.80 1333.2114 1312.0385 1213.2736
Hi.95 1481.6383 1460.9272 1429.1281


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()
Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)

Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))

Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")

Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))

Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
  mutate(value = Tami + Andrés) %>%
  rename(ds = mes_ano, y = value) %>%
  mutate(#ds= format(ds, "%Y-%m"),
         unique_id = "1") %>% #it is only one series
  select(unique_id, ds, y)

#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
  dplyr::rename(ds = date3, y = value) %>%
  dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
  dplyr::mutate(unique_id = "serie_1")%>%
  dplyr::select(unique_id, ds, y) %>%
  mutate(ds = ymd(ds)) %>%  # Convert 'ds' to Date
  mutate(month = month(ds), year = year(ds)) %>%  # Extract month and year
  group_by(month, year) %>%  # Group by month and year
  summarise(average_y = mean(y))%>%  # Calculate average y
  mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
  ungroup()%>%
  select(ds, uf=average_y)

Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |> 
  dplyr::left_join(uf_timegpt_my, by=c("ds"="ds")) 

#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
  Gastos_casa_mensual_2022_timegpt_ex,
  h = 12,
  freq = "M",  # Monthly frequency
  add_history = TRUE,
  level = c(80, 95),
  model = "timegpt-1",#"timegpt-1-long-horizon",
  clean_ex_first = TRUE
)

# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
  mutate(ds = as.Date(paste0(ds, "-01")))  # Add day to make it a complete date

gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
  mutate(ds = as.Date(paste0(ds, "-01")))  # Add day to make it a complete date

# Combine historical and forecast data
full_data_gastos <- bind_rows(
  Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
  gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)

full_data_gastos |> 
  dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |> 
# Visualize results
ggplot(aes(x = ds, y = y)) +
  geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
              fill = "#4B9CD3", alpha = 0.2) +
  geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
              fill = "#4B9CD3", alpha = 0.3) +
  geom_line(aes(color = type), linewidth = 1.5) +
  geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
             linetype = "dashed", color = "red", linewidth = 0.8) +
  scale_x_date(
    date_breaks = "3 months",
    date_labels = "%b %Y"
  ) +
  scale_y_continuous(
    name = "Gastos Totales",
    labels = scales::comma,
    breaks = pretty(full_data_gastos$y, n = 10),
    expand = expansion(mult = c(0.05, 0.05))
  ) +
  scale_color_manual(
    name = "Leyenda",
    values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
  ) +
  labs(
    title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
    subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
    x = "Fecha",
    y = "Gastos Totales",
    color = "Leyenda"
  ) +
  theme_minimal() +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    axis.title.x = element_text(size = 10),
    axis.title.y = element_text(size = 10),
    legend.position = "bottom",
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank()
  )


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 26100)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## system code page: 65001
## 
## time zone: UTC
## tzcode source: internal
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] prophet_1.0        rlang_1.1.6        Rcpp_1.1.0         scales_1.4.0      
##  [5] ggiraph_0.9.1      tidytext_0.4.3     DT_0.34.0          autoplotly_0.1.4  
##  [9] rvest_1.0.5        plotly_4.11.0      xts_0.14.1         forecast_8.24.0   
## [13] wordcloud_2.6      RColorBrewer_1.1-3 SnowballC_0.7.1    tm_0.7-16         
## [17] NLP_0.3-2          tsibble_1.1.6      lubridate_1.9.4    forcats_1.0.1     
## [21] dplyr_1.1.4        purrr_1.1.0        tidyr_1.3.1        tibble_3.3.0      
## [25] ggplot2_4.0.0      tidyverse_2.0.0    sjPlot_2.9.0       lattice_0.22-6    
## [29] gridExtra_2.3      plotrix_3.8-4      sparklyr_1.9.2     httr_1.4.7        
## [33] readxl_1.4.5       zoo_1.8-14         stringr_1.5.2      stringi_1.8.7     
## [37] DataExplorer_0.8.4 data.table_1.17.8  reshape2_1.4.4     fUnitRoots_4040.81
## [41] plyr_1.8.9         readr_2.1.5       
## 
## loaded via a namespace (and not attached):
##   [1] rstudioapi_0.17.1     jsonlite_2.0.0        magrittr_2.0.4       
##   [4] farver_2.1.2          rmarkdown_2.30        vctrs_0.6.5          
##   [7] askpass_1.2.1         janitor_2.2.1         htmltools_0.5.8.1    
##  [10] curl_7.0.0            janeaustenr_1.0.0     cellranger_1.1.0     
##  [13] Formula_1.2-5         TTR_0.24.4            StanHeaders_2.32.10  
##  [16] parallelly_1.45.1     sass_0.4.10           KernSmooth_2.23-22   
##  [19] bslib_0.9.0           htmlwidgets_1.6.4     tokenizers_0.3.0     
##  [22] extraDistr_1.10.0     httr2_1.2.1           cachem_1.1.0         
##  [25] uuid_1.2-1            networkD3_0.4.1       igraph_2.1.4         
##  [28] lifecycle_1.0.4       pkgconfig_2.0.3       Matrix_1.7-0         
##  [31] R6_2.6.1              fastmap_1.2.0         future_1.67.0        
##  [34] snakecase_0.11.1      digest_0.6.37         selectr_0.4-2        
##  [37] colorspace_2.1-2      spatial_7.3-17        crosstalk_1.2.2      
##  [40] labeling_0.4.3        timechange_0.3.0      abind_1.4-8          
##  [43] compiler_4.4.0        bit64_4.6.0-1         withr_3.0.2          
##  [46] inline_0.3.21         S7_0.2.0              tseries_0.10-58      
##  [49] carData_3.0-5         DBI_1.2.3             QuickJSR_1.8.1       
##  [52] pkgbuild_1.4.8        gplots_3.2.0          openssl_2.3.4        
##  [55] rappdirs_0.3.3        loo_2.8.0             fBasics_4041.97      
##  [58] gtools_3.9.5          caTools_1.18.3        tools_4.4.0          
##  [61] lmtest_0.9-40         quantmod_0.4.28       future.apply_1.20.0  
##  [64] nnet_7.3-19           glue_1.8.0            quadprog_1.5-8       
##  [67] nlme_3.1-164          nixtlar_0.6.2         generics_0.1.4       
##  [70] gtable_0.3.6          tzdb_0.5.0            hms_1.1.3            
##  [73] xml2_1.4.0            car_3.1-3             pillar_1.11.1        
##  [76] vroom_1.6.6           bit_4.6.0             tidyselect_1.2.1     
##  [79] its.analysis_1.6.0    knitr_1.50            urca_1.3-4           
##  [82] stats4_4.4.0          xfun_0.53             matrixStats_1.5.0    
##  [85] timeDate_4041.110     rstan_2.32.7          lazyeval_0.2.2       
##  [88] yaml_2.3.10           boot_1.3-30           evaluate_1.0.5       
##  [91] codetools_0.2-20      timeSeries_4041.111   data.tree_1.2.0      
##  [94] cli_3.6.5             RcppParallel_5.1.11-1 systemfonts_1.3.1    
##  [97] jquerylib_0.1.4       globals_0.18.0        dbplyr_2.5.1         
## [100] anytime_0.3.12        parallel_4.4.0        ggfortify_0.4.19     
## [103] ellipsis_0.3.2        fracdiff_1.5-3        bitops_1.0-9         
## [106] listenv_0.9.1         viridisLite_0.4.2     slam_0.1-55          
## [109] crayon_1.5.3
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))